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Definition of hypothesis

Did you know.

The Difference Between Hypothesis and Theory

A hypothesis is an assumption, an idea that is proposed for the sake of argument so that it can be tested to see if it might be true.

In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.

A hypothesis is usually tentative; it's an assumption or suggestion made strictly for the objective of being tested.

A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, it is understood to be more likely to be true than a hypothesis is.

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch, with theory being the more common choice.

Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.

The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)

This mistake is one of projection: since we use theory in general to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.

The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."

proposition
supposition

hypothesis , theory , law mean a formula derived by inference from scientific data that explains a principle operating in nature.

hypothesis implies insufficient evidence to provide more than a tentative explanation.

theory implies a greater range of evidence and greater likelihood of truth.

law implies a statement of order and relation in nature that has been found to be invariable under the same conditions.

Examples of hypothesis in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'hypothesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Greek, from hypotithenai to put under, suppose, from hypo- + tithenai to put — more at do

1641, in the meaning defined at sense 1a

Phrases Containing hypothesis

counter - hypothesis
nebular hypothesis
null hypothesis
planetesimal hypothesis
Whorfian hypothesis

Articles Related to hypothesis

This is the Difference Between a...

This is the Difference Between a Hypothesis and a Theory

In scientific reasoning, they're two completely different things

Dictionary Entries Near hypothesis

hypothermia

hypothesize

Cite this Entry

“Hypothesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/hypothesis. Accessed 3 May. 2024.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk, "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

Operationalization

Hypothesis Types

Hypotheses examples.

Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

Forming a question
Performing background research
Creating a hypothesis
Designing an experiment
Collecting data
Analyzing the results
Drawing conclusions
Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you expect to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment do not support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

Is your hypothesis based on your research on a topic?
Can your hypothesis be tested?
Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

Collect as many observations about a topic or problem as you can.
Evaluate these observations and look for possible causes of the problem.
Create a list of possible explanations that you might want to explore.
After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method , falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that if something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

Does your hypothesis focus on something that you can actually test?
Does your hypothesis include both an independent and dependent variable?
Can you manipulate the variables?
Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the dependent variable if you change the independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

"Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
"Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."
"Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
"Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

"People with high-sugar diets and sedentary activity levels are more likely to develop depression."
"Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

"There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
"There is no difference in scores on a memory recall task between children and adults."
"There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

"People who take St. John's wort supplements will have less anxiety than those who do not."
"Adults will perform better on a memory task than children."
"Children who play first-person shooter games will show higher levels of aggression than children who do not."

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as case studies , naturalistic observations , and surveys are often used when conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a correlational study can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually cause another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses . R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:]. Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ? PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies . Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Definition of 'hypothesis'

hypothesis in British English

Hypothesis in american english, examples of 'hypothesis' in a sentence hypothesis, cobuild collocations hypothesis, trends of hypothesis.

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In other languages hypothesis

American English : hypothesis / haɪˈpɒθɪsɪs /
Brazilian Portuguese : hipótese
Chinese : 假设
European Spanish : hipótesis
French : hypothèse
German : Hypothese
Italian : ipotesi
Japanese : 仮説
Korean : 가설
European Portuguese : hipótese
Latin American Spanish : hipótesis
Thai : สมมุติฐาน

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What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By: Derek Jansen (MBA) | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing.

Research Hypothesis 101

What is a hypothesis ?
What is a research hypothesis (scientific hypothesis)?
Requirements for a research hypothesis
Definition of a research hypothesis
The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

A good research hypothesis needs to be very clear about what’s being assessed and very specific about the expected outcome.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference.

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

A good research hypothesis must be testable. In other words, you must able to collect observable data in a scientifically rigorous fashion to test it.

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell.

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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What Is Research Methodology? Simple Definition (With Examples) - Grad Coach - […] Contrasted to this, a quantitative methodology is typically used when the research aims and objectives are confirmatory in nature. For example,…

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[ hahy- poth - uh -sis , hi- ]

a proposition, or set of propositions, set forth as an explanation for the occurrence of some specified group of phenomena, either asserted merely as a provisional conjecture to guide investigation working hypothesis or accepted as highly probable in the light of established facts.
a proposition assumed as a premise in an argument.
the antecedent of a conditional proposition.
a mere assumption or guess.

/ haɪˈpɒθɪsɪs /

a suggested explanation for a group of facts or phenomena, either accepted as a basis for further verification ( working hypothesis ) or accepted as likely to be true Compare theory
an assumption used in an argument without its being endorsed; a supposition
an unproved theory; a conjecture

/ hī-pŏth ′ ĭ-sĭs /

, Plural hypotheses hī-pŏth ′ ĭ-sēz′

A statement that explains or makes generalizations about a set of facts or principles, usually forming a basis for possible experiments to confirm its viability.
plur. hypotheses (heye- poth -uh-seez) In science, a statement of a possible explanation for some natural phenomenon. A hypothesis is tested by drawing conclusions from it; if observation and experimentation show a conclusion to be false, the hypothesis must be false. ( See scientific method and theory .)

Discover More

Derived forms.

hyˈpothesist , noun

Other Words From

hy·pothe·sist noun
counter·hy·pothe·sis noun plural counterhypotheses
subhy·pothe·sis noun plural subhypotheses

Word History and Origins

Origin of hypothesis 1

Synonym Study

Example sentences.

Though researchers have struggled to understand exactly what contributes to this gender difference, Dr. Rohan has one hypothesis.

The leading hypothesis for the ultimate source of the Ebola virus, and where it retreats in between outbreaks, lies in bats.

In 1996, John Paul II called the Big Bang theory “more than a hypothesis.”

To be clear: There have been no double-blind or controlled studies that conclusively confirm this hair-loss hypothesis.

The bacteria-driven-ritual hypothesis ignores the huge diversity of reasons that could push someone to perform a religious ritual.

And remember it is by our hypothesis the best possible form and arrangement of that lesson.

Taken in connection with what we know of the nebulæ, the proof of Laplace's nebular hypothesis may fairly be regarded as complete.

What has become of the letter from M. de St. Mars, said to have been discovered some years ago, confirming this last hypothesis?

To admit that there had really been any communication between the dead man and the living one is also an hypothesis.

"I consider it highly probable," asserted Aunt Maria, forgetting her Scandinavian hypothesis.

Related Words

explanation
interpretation
proposition
supposition

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hypothesist noun

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What does the noun hypothesist mean?

There is one meaning in OED's entry for the noun hypothesist . See ‘Meaning & use’ for definition, usage, and quotation evidence.

Entry status

OED is undergoing a continuous programme of revision to modernize and improve definitions. This entry has not yet been fully revised.

How common is the noun hypothesist ?

Where does the noun hypothesist come from.

Earliest known use

The earliest known use of the noun hypothesist is in the late 1700s.

OED's only evidence for hypothesist is from 1788, in the writing of Thomas Jefferson, revolutionary politician and president of the United States of America.

hypothesist is formed within English, by derivation.

Etymons: hypothesis n. , ‑t suffix 3

Nearby entries

hypothecary, adj. 1656–
hypothecate, v. 1693–
hypothecation, n. 1681–
hypothecative, adj. 1856–
hypothecator, n. 1828–
hypothecium, n. 1866–
hypothenar, adj. 1706–
hypothermia, n. 1886–
hypothermic, adj. 1898–
hypothesis, n. 1596–
hypothesist, n. 1788–
hypothesize, v. 1738–
hypothesizer, n. 1833–
hypothetic, adj. & n. a1680–
hypothetical, adj. & n. 1588–
hypothetically, adv. 1628–
hypothetico-deductive, adj. 1912–
hypothetico-deductively, adv. 1953–
hypothetico-disjunctive, adj. & n. a1856–
hypothetist, n. 1852–
hypothetize, v. 1895–

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Meaning & use

Entry history for hypothesist, n..

Originally published as part of the entry for hypothesis, n.

hypothesis, n. was first published in 1899; not yet revised.

Revision of the OED is a long-term project. Entries in oed.com which have not been revised may include:

corrections and revisions to definitions, pronunciation, etymology, headwords, variant spellings, quotations, and dates;
new senses, phrases, and quotations which have been added in subsequent print and online updates.

Earlier versions of this entry were published in:

OED First Edition (1899)

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View hypothesis in OED Second Edition

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experimental hypothesis

Quick reference.

The hypothesis that in an experiment, the results of the experimental group will differ significantly from those of a control group, and that the difference will be caused by the independent variable (or variables) under investigation. Compare null hypothesis.

From: experimental hypothesis in The Oxford Dictionary of Sports Science & Medicine »

Subjects: Medicine and health — Clinical Medicine

7 The Predictive Processing Hypothesis

Jakob Hohwy Cognition & Philosophy Lab, Department of Philosophy, Faculty of Arts, Monash University, Melbourne, Australia

Published: 09 October 2018
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Prediction may be a central concept for understanding perceptual and cognitive processing. Contemporary theoretical neuroscience formalizes the role of prediction in terms of probabilistic inference. Perception, action, attention, and learning may then be unified as aspects of predictive processing in the brain. This chapter first explains the sense in which predictive processing is inferential and representational. Then follows an exploration of how the predictive processing framework relates to a series of considerations in favor of enactive, embedded, embodied, and extended cognition (4E cognition). The initial impression may be that predictive processing is too representational and inferential to fit well to 4E cognition. But, in fact, predictive processing encompasses many phenomena prevalent in 4E approaches, while remaining both inferential and representational.

Introduction

A millennium ago the great polymath Ibn al Haytham (Alhazen) (ca. 1030; 1989) developed the view that “many visible properties are perceived by judgment and inference” (II.3.16). He knew that there are optical distortions and omissions of the image hitting the eye, which without inference would make perception as we know it impossible ( Lindberg 1976 ; Hatfield 2002 ). Al Haytham was aware it is counterintuitive to say perception depends on typically intellectual activities of judgment and inference and so remarks that “the shape and size of a body . . . and such like properties of visible objects are in most cases perceived extremely quickly, and because of this speed one is not aware of having perceived them by inference and judgment” (II.3.26).

Since al Haytham, many in optics, psychology, neuroscience, and philosophy have advocated the role of inference in perception, and have insisted too that this inference is somehow unconscious (for review, see Hatfield 2002 ). With characteristic clarity, Hermann von Helmholtz coined the phrase unconscious perceptual inference and said that the “psychical activities” leading to perception:

are in general not conscious, but rather unconscious. In their outcomes they are like inferences insofar as we from the observed effect on our senses arrive at an idea of the cause of this effect. This is so even though we always in fact only have direct access to the events at the nerves, that is, we sense the effects, never the external objects. ( Helmholtz 1867 , p. 430)

The starting point for this inferential view is the conviction that perception can be explained only if a particular, fundamental problem of perception is solved, namely, how the brain can construct our familiar perceptual experience on the basis only of the imperfect data delivered to the senses, and without ever having unfettered access to the true hidden causes of that input. This type of problem is also at the heart of massive scientific endeavors in contemporary artificial intelligence and machine learning.

Recently, the notion of unconscious perceptual inference has been embedded in a vast probabilistic theoretical framework covering cognitive science, theoretical neurobiology, and machine learning. The basic idea is that unconscious perceptual inference is a matter of Bayesian inference, such that the brain in some manner follows Bayes’s rule and thereby can overcome the problem of perception. The most comprehensive, ambitious, and fascinating of these probabilistic theories build on the notion of prediction error minimization (PEM) (this notion arose in machine learning research, with versions of it going back to 1950s; for recent philosophical overviews, see Clark 2013 ; Hohwy 2013 ).

Several aspects of unconscious perceptual inference are anathema to many versions of enactive, embedded, embodied, and extended (4E) cognition. If perception is a matter of Bayesian inference, then perception seems a very passive, intellectualist, neurocentric phenomenon of receiving sensory input and performing inferential operations on them in order to build internal representations. This process is divorced from action and active interaction with the environment; it appears insensitive to the situation in which the system is embedded; it leaves no foundational role for the body in cognitive and perceptual processes; and it makes perceptual processes a matter of what happens behind the sensory veil with no possibility of extension to mental states beyond the brain, let alone the body (4E cognition is now a vast and varied area of research; the types of approaches that stress anti-representational and anti-inferential elements are, for example, Varela et al. 1991 ; Clark 1997 ; Noë 2004; Gallagher 2005 ; Thompson 2007 ; Clark 2008 ; Hutto and Myin 2013 ).

The tension between perceptual inference and 4E cognition matters because both are influential attempts at explaining the same range of phenomena. Having noticed the initial tension between them, there are three main options: (1) perceptual inference and 4E cognition are incompatible as foundational accounts of perception and cognition, which means one must be false ( Anderson and Chemero 2013 ; Barrett 2015 ); this option appears unattractive because key aspects of both seem believable and important. The next two options are more discursive: (2) perceptual inference and 4E cognition should be considered compatible, but only because perceptual inference, rightly understood, is not a matter of neurocentric, representationalist inference but yields just the kinds of processes necessary for 4E cognition (Clark 2013 , 2015 , 2016 ). (3) Perceptual inference and 4E cognition should be considered compatible, but only because 4E cognition, rightly understood, is nothing but representation and inference ( Hohwy 2016b ). Options 2 and 3 deflate perceptual inference and 4E cognition, respectively, that is, they achieve reconciliation by recasting one of the sides of the debate in terms of the other.

This chapter aims to show that Option 3 is reasonable. Perceptual inference, in the shape of PEM, is tremendously resourceful and can therefore encompass phenomena highlighted in debates on 4E cognition. Reconciliation with somewhat deflated 4E notions is achieved without compromising PEM’s representationalist and inferentialist essence. This advances the debate about 4E cognition because, in the context of PEM, inference and representation are both shown to have several surprising aspects, such that, perhaps, 4E cognition need not abhor these notions altogether.

The chapter first explains PEM and lays out its specific notion of inference. Then action is subsumed under PEM’s inferential scheme, and the role of representation in perception and action is explained. Finally, select aspects of 4E cognition are incorporated into the PEM fold.

Predictive Processing and Inference

In many approaches to unconscious perceptual inference, the notion of inference is left unspecified; as Helmholtz says, our psychical activities are “like” inference. Here, the notion of inference captures the idea that the perceptual and cognitive systems need to draw conclusions about the true hidden causes of sensory input vicariously, working only from the incomplete information given in the sensory input.

On modern approaches, this is given shape in terms of Bayesian inference. This yields a concrete sense of “inference” where Bayes’s rule is used to update internal models of the causes of the input in the light of new evidence. A Bayesian system will arrive at new probabilistically optimal “conclusions” about the hidden causes by weighting its prior expectations about the causes against the likelihood that the current evidence was caused by those causes (there are useful textbook sources on machine learning such as Bishop 2007 ; philosophical reviews such as Rescorla 2015 ; see also recent treatments of hierarchical Bayes and volatility such as Payzan-LeNestour and Bossaerts 2011 ; Mathys et al. 2014 ).

Consider a series of sensory samples, for example, auditory inputs drawn from a sound source. The question for the perceiver is where the sound source is located (somewhere on a 180° space in front of the perceiver). Assume the samples are normally distributed and that the true source is 80°. Before any samples come in, the perceiver expects—predicts—samples to be distributed around 90°. The first sample comes in indicating 77°, and thereby suggests a prediction error of 13°. Which probabilistic inference should the perceiver make? Inferring that the source is at 77° would disregard prior knowledge and lead to a model overfitted to noise. Ignoring the prediction error would prevent perceptual learning altogether. So the right weight to assign to the prediction error in updating the prior belief of 90° ought to reflect an optimal, rational balance between the prior and the likelihood, and this is indeed what Bayes’s rule delivers. So probabilistic inference should be determined by Bayes’s rule. In other words, the learning rate in Bayesian inference is determined by how much is already known and how much is being learned by the current evidence, reflected in the likelihood. (In this toy example, I set aside the question how the perceiver knows not to add the weighted prediction error to 90°, moving toward 103° and away from 80°; notice that if the system does this, then prediction error will tend to grow over time).

The correct weights to give to the prior and the prediction error can be considered transparently through the variance of their probability distributions. The more the variance, the less the weight. A strong prior will have little variance and should be weighted highly, and a precise input, which fits well the expected values of the model in question, should be weighted highly. The inverse of the variance is called the precision , and it is a mathematically expedient convention to operate with precisions in discussions of inference: the learning rate in Bayesian inference therefore depends on the precisions of the priors and prediction errors. As will become apparent later, precisions are important to PEM and its ability to engage 4E-type issues.

So far, only one inferential step is described. For subsequent samples, Bayes’s rule should also be applied, but for the old inferred posterior as the new prior. Since there is an optimal mix of prior and likelihood, the model will converge on the true mean (80°) in the long run. Critically, in this process, the average prediction error is minimized over the long run. Even for quite noisy samples (imprecise distributions, or probability density functions), a Bayesian inference system will eventually settle on an expectation for the mean that keeps prediction error low. This can be turned around such that, subject to a number of assumptions about the shape of the probability distributions and the context in which they are considered, a system that minimizes prediction error in the long run will approximate Bayesian inference.

The heart of PEM is then the idea that a system need not explicitly know or calculate Bayes’s rule to approximate Bayesian inference. All the system needs is the ability to minimize prediction error in the long run. This is the sense in which unconscious perceptual inference is inference: internal models are refined through prediction error minimization such that Bayesian inference is approximated. The notion of inference is therefore nothing to do with propositional logic or deduction, nor with overly intellectual application of theorems of probability theory.

It would be misguided to withdraw the label “inference” from unconscious perceptual inference, or from PEM, just because it is an approximation to Bayes, or because the process is not an explicit application of a mathematical formalism by the brain. If the inferential aspect is not kept in focus, then it would appear to be a coincidence, or somehow an optional aspect of perceptual and cognitive processes that conform to what Bayes’s rule dictate. Put differently, anyone who subscribes to the notion of predictive processing must also accept the inferential aspect. If it is thrown out, then the “prediction error minimization” part becomes a meaningless, unconstrained notion.

PEM thus says that perceivers harbor internal models that give rise to precision-weighted predictions of what the sensory input should be, and that these predictions can be compared to the actual sensory input. The ensuing prediction error guides the updates of the internal model such that prediction error in the long run is minimized and Bayesian inference approximated.

However, this description of PEM is still too sparse. In any given situation, a PEM system will not know how much or how little to weight prediction error even if it can assess the precisions of the prior and of the current prediction error. In essence, a system that operates with only those precisions will be assuming the world is more simple and persistent than it really is. For example, different sensory modalities have different precisions in different contexts, and without prior knowledge of these precisions, the system can make no informed decisions about how to weight prediction error. For example, similarly sized prediction errors in the auditory and visual modalities should not be weighted the same, since the precisions of each should be expected to be different. Therefore a PEM system would need to have and shape expectations about the precisions as well as the means of probability distributions. The need for such expected precisions is also driven by the occurrence of multiple interacting causes of sensory input within and across sensory modalities. In the example of the location of the auditory source, variability in the sensory sampling might be due to a new cause interfering with the original sound source (e.g., a moving screen intermittently obscures the location of the sound). If the system does not have robust expectations for the precision of the sound source, then it will be unable to make the right inferences about the input (i.e., is it one cause with varying precisions or is it two interacting causes that gives rise to the nonlinear evolution in the auditory sensory input?).

A PEM system must model expectations of precisions, and this part of the PEM system itself needs to be Bayes-optimal. Models will harbor priors for precisions; they will predict precisions and generate precision prediction errors. Moreover, they will need to do this across all the hidden causes modeled such that their interactions can be taken into account. This calls for a hierarchical structure where the occurrence of various causes over many different time scales can impact on the predictions of the sensory input received at any given time. For example, the interaction of relatively slow time scale regularities (e.g., the trains driving past your house two or three times an hour) need to influence the predictions of faster time scale regularities (e.g., the words heard in a conversation in your lounge room), and vice versa.

A PEM system that operates in a complex environment, with levels of uncertainty that depend on the current state of the world and many interacting causes at many different time scales, will thus build up a vast internal model with many interacting, hierarchically ordered levels, which all pass messages to each other in an attempt to minimize average prediction error over the long term.

Consider finally what happens over time to the models harbored in the brain, on the basis of which predictions are made and prediction errors minimized. The parameters of these models will be shaped by the Bayesian inferential process to mirror the causes of the sensory input. In the example earlier, by minimizing prediction error over time for the location of the cause of auditory input, the model will revise its initial false belief that the location is at 90°, and come to expect it to be at its true position of 80°. Further, by minimizing precision prediction error, the model may be able to anticipate interacting causes, such as a moving screen intermittently blocking the sound. This means that, by approximating Bayesian inference, the models of a PEM system must represent its world.

Here, the notion of representation is not just a matter of receptor covariance, where the states of neural populations covary with the occurrence of certain environmental causes. The hierarchical model is highly structured, and performs operations over the parameters. For example, there will be model selection. In our example, the system might ask whether there is another cause interacting with the sound source, or if the signal itself is becoming noisier. In addition, there are convolutions of separate expected signals generated on the basis of the models; for example, when a cat and a fence are detected, the expected sensory signals from both hidden causes are convolved into one stream by the brain to take the occlusion of the cat by the fence into account. As will become clear, the representational aspects of PEM are critical when it comes to incorporating action, too.

The representational nature of a PEM system is not optional. The ability to minimize prediction error over time depends on building better and better representations of the causes of its sensory input. This is encapsulated in the very notion of model revision in Bayesian inference. (There is extensive discussion of what it takes for perception to be representational; for examples of relevance to Bayesian inference, see Ramsey 2007 ; Orlandi 2013 , 2014 ; Gładziejewski 2015; Ramsey 2015 .)

So far, it appears that predictive processing is inferential and representational in a specific Bayesian sense. Traditionally, 4E approaches have rejected both notions. Next, PEM will be shown to have explanatory reach into 4E cognition too.

PEM and Action

A representationalist and inferentialist account of cognition and perception may appear divorced from the concerns and activities of a real, embodied agent operating in its environment. Thus enactive and embodied accounts have de-emphasized classic representationalist understandings of cognition and perception and with it much semblance to inference (there are many versions and much discussion of embodiment; see, e.g., Brooks 1991 ; Noë 2004; Gallagher 2005 ; Alsmith and Vignemont 2012 ; Hutto and Myin 2013 ; Orlandi 2014 ).

Perhaps the basic sentiment could be summed up in the strong intuition that embodied action is not inference, and yet the body and its actions are crucial to gain any kind of understanding of perception and cognition. PEM can, however, easily cast action as a kind of inference—as active inference (Friston, Samothrakis, et al. 2012).

Recall that any system that minimizes prediction error over time will approximate Bayesian inference; that is, such a system will be inferential in the Bayesian sense that it increases the evidence for its internal model. Using the example from earlier again, by minimizing prediction error the system could accumulate evidence for the model that represents the sound source as located at 80°. In that case, the internal model is revised from the initial 90° to the new estimate of 80°.

It is trivial to observe that the perceiver could also have minimized prediction error by turning the head 10° to the left and thereby have accumulated evidence for the prediction that the sound source is located at 90°. Prediction error can be minimized both through passive updating of the internal model and through active changes to the sensory input. Action, such as turning one’s head, can therefore minimize prediction error. Since, as argued earlier, minimizing prediction error is inference, and action is inference. There is then no hindrance to incorporating action into an inferentialist framework.

In active inference, representations are central to guiding action. This is because action only occurs when a hypothesis—in this case a representation of a state that is yet to occur—has accumulated sufficient evidence relative to other hypotheses to become the target of PEM. This yields two aspects that are sometimes seen as hallmarks of representations: they are action-guiding and they are somehow detached from what they stand for (for discussion and review, see Orlandi 2014 ). Active inference therefore has a good claim to be both inferential and representational.

For perceptual inference, precisions were shown to be critical. Without precisions, the PEM system would not be able to minimize error in a world with state-dependent uncertainty and interacting causes. The same holds for active inference. Without any notion of how levels of prediction error tend to shift over many interacting time scales, the system would pick the action that minimizes most error here and now—for example, by entering and remaining in a dark room (for discussion, see Friston, Thornton, et al. 2012). This would be analogous to overfitting, and would come at the cost of increasing prediction error over the longer term. For example, even though the perceiver might minimize prediction error by forcing the sound to come at the 90° midline, this might make it difficult to ascertain the true source of a potentially moving cause such as the trajectory of a mosquito buzzing about (since direction detection is harder over the midline due to minimal interaural time difference). This calls for even more hierarchical model-building, namely, in terms of the precisions expected in the evolution of the prediction error landscape as a result of the agent’s active intervention in the world. These self-involving, modeled regularities are, however, not fundamentally different from the regularities involved in perceptual inference. They simply concern the sensory input the agent should expect to result from the interaction of one particular cause in the world—the agent itself—with all the other causes of sensory input (for discussion of self-models, see, e.g., Synofzik et al. 2008 ; Metzinger 2009 ).

There is thus room for a notion of action within PEM. But this possibility alone does not imply that a PEM system is likely to actually be an agent. If the system is endowed with a body such that it could act, then the imperative for minimization of prediction error will make actual action highly likely.

If the system has accumulated strong evidence for, say, an association between two sounds, it may still be unable to distinguish several hypotheses, for example, whether the sounds are related as cause and effect or if they are effects of some common cause. It is standard in the causal inference literature that intervention is required to acquire evidence for or against these hypotheses ( Pearl 2000 ; Woodward 2003 ). For example, if variation in one sound persists even if the other sound is actively switched off, then that is evidence the latter sound is not the cause of the first. The necessity of action is generalized in the observation from earlier that the system needs to learn differences in precisions and patterns of interactions among causes, such as occlusions and other causal relations that change the sensory input in nonlinear ways. Such learning thus requires action. The price of not engaging the body plant to intervene in the environment is that prediction error will tend to increase since predictions will be unable to distinguish between several different hypotheses. A PEM system that can act will therefore be best served to actually act.

This simple account of agency has profound consequences. It will be a learnable pattern in nature that inaction will tend to increase prediction error in the longer term (due to the inaccuracy of the hypotheses the system can accumulate evidence for by using only passive inference). Conversely, the system can learn that action tends to allow minimization of prediction error at reasonable time scales. Overall, this teaches the system that, on balance, its model will accumulate more precise evidence through action than through inaction. This will bias it to minimize prediction error through active inference. Of course, a system that only ever acts on the basis of unchanging models will never be able to learn new patterns, which is detrimental in a changing world. Therefore action must be interspersed with perceptual inference where models are updated, before new action takes place.

The mechanism by which this switching between perception and action takes place is best conceived in terms of precision optimization. Recall that the PEM system will build up expectations for precisions, which are crucial for dealing with state-dependent noise in a world with interacting causes. The role of expected precisions in inference is to optimally adjust weights for expected sensory input: input that is expected to be precise is favored in Bayesian inference whereas input that is expected to be imprecise is not favored. Mechanistically, this calls for a neuronal gating mechanism that inhibits or excites sensory inputs according to their expected precisions. This gating mechanism serves as a kind of probabilistic searchlight and thus plays the functional role of attention ( Feldman and Friston 2010 ; Brown et al. 2011 ; Hohwy 2012 , 2016a ).

As the system gates its sensory input according to where it expects the most precise sensory input will occur, across several time scales, it may switch between perception and action. For example, if more precision is expected by the agent having its hand at the position of the coffee cup rather than at the current position at the laptop, then it will begin gating the current sensory input, which suggests the hand is at the laptop. This in turn allows the coffee hypothesis to gain relative weight over the laptop hypothesis, and the prediction error generated by that hypothesis can easily by minimized by moving the hand. Since the gain is high on this prediction error, the new hypothesis quickly accumulates evidence for its truth, and the hand will find itself at the coffee cup (for more on the dynamics of action and perception in relation to temporal phenomenology, see Hohwy et al. 2015 ; for the formal background, see Friston, Trujillo-Barreto et al. 2008).

Embodied, Embedded, and Inferential and Representational

When all the elements described in the last section are combined, a wholly inferential conception of agency begins to take shape. If action and agency are moments of PEM, then desires are just beliefs (or priors) about states that happen to be future, with a focus on their anticipated levels of prediction error, and where reward is the absence of prediction error. This suggests a neat continuity with perceptual inference, which also relies on priors and the imperative to minimize prediction error.

The idea that action is driven by PEM relative to a model does raise a question about the content of the model relative to which error is minimized. This model is what defines what we would normally describe as the agent’s desires. In the wider PEM framework—which, as shall be described later, relies on notions of free energy minimization —the expected states that anchor active inference relate to set points in terms of the organism’s homeostasis. This immediately evokes an evolutionary perspective, where expected bodily states are central to behavior. Apart from the specific evolutionary aspects, this suggests an embodiment perspective, because all aspects of perception and cognition then have a foundation in bodily states, and movement and purposeful behavior have a foundation in the environment. This element of embodiment makes it more likely that contact can be made between probabilistic theories of perception and action and embodied cognition approaches (such as, e.g., Varela et al. 1991 ; Gallagher 2005 ; Thompson 2007 ; for recent treatments that relate to PEM, see Bruineberg and Rietveld 2014 ; Fazelpour and Thompson 2015 ).

However, even this foundational embodiment is conceived probabilistically in PEM. A set of expectations for bodily states (relating to homeostasis) is essentially a model. In probabilistic terms, this model gives the probability of finding the organism in some subset of the overall set of states it could be in. The model is specified in terms of internal states, as signaled in interoception, but is tied to the overall setting of the organism in a subset of environmental states. The expected states defined in interoceptive terms would, in real organisms traversing actual environments, be mirrored in the expected states described in environmental terms, or in terms of their sensory input or exteroception. For example, fish are most likely to find their sensory organs impinged upon from watery states and this is associated strongly with the homeostatic needs specified in their model. In general, within this probabilistic reading of the foundational embodiment of a PEM organism, there is thus a tight coupling between the interoceptive and exteroceptive prediction error landscapes for any PEM system.

Not only does PEM provide a notion of embodiment, it also speaks to elements of embedded or situated cognition (see van Gelder 1995 ; Clark 1997 ; Aydede and Robbins 2009 ). With the tight coupling of the organism’s expected states in terms of interoception and exteroception, perception and cognition cannot be separated from bodily or environmental aspects of the PEM system.

Crucially, this reading of embodiment and embedding leads directly to inferential processing and PEM. The model specifies the probability of finding the organism in any one of all the possible states. To know this model directly would require the agent averaging over all possible states and ascertaining the occurrence of itself in them. This is not possible for a finite organism to learn directly. Instead, the organism must essentially guess what its expected states are and minimize the ensuing error through perceptual and active inference. In slightly more formal terms, the organism needs to minimize surprise; that is, it needs to avoid finding itself in states that are surprising given its model. The sum of prediction error is always equal to or larger than the surprise, so minimizing prediction error will implicitly minimize surprise. This bound on surprise is also known in probabilistic terms as the free energy, and so this challenging idea is enshrined in the so-called free energy principle ( Friston 2010 ).

When viewed in this larger context of the free energy principle, promising notions of embodied and embedded cognition present themselves. More research is needed on the extent to which they capture facets of the wide-ranging and heterogeneous 4E body of research. However, for the conception of embodiment and embedding mooted here, an inferential conception is inescapable.

Hierarchical Inference for a Changing World

In much 4E research there is a focus on fluid interactions with the world, characterized by non-inferential, nonrepresentational, “quick and dirty” processing. This picture is set up to contrast with inferential, representational, “slow and clean” processing (Clark 1997 , 2013 , 2015 ). Often, this kind of quick and dirty, situated cognition is discussed in terms of affordances : salient elements of the environment that are in some sense perceived directly and are immediately action-guiding. Affordances in quick and dirty processing are thought to evade the computational bottleneck that a traditional representational system would have trying to passively encode the entire sensory input presented at any given time. For some types of action and at some stages of learning, performance is rather plodding and sluggish, but there is an important insight in how the notion of situated cognition highlights the fluid swiftness with which organisms can perform some complex actions in their environment.

In a PEM system there is no bottleneck problem in the first place, however. There is never an issue of starting from scratch and encoding an entire natural scene in order to be able to perceive it. Hierarchical Bayesian inference is based on prior learning, which over time has shaped priors at many levels. Given priors, the sensory input is no longer something that needs to be encoded here and now. Instead the sensory input is, functionally speaking, the feedback to the forward predictive signal generated by the brain’s internal model ( Friston 2005 ). The model predicts what will happen and gets confirmation or disconfirmation on these predictions from the sensory input. There is thus no encoding of the entire sensory input in each perceptual instance. This means the PEM system has no need to resort to quick and dirty processing tricks to overcome a computational bottleneck. Instead, the system relies on slow and clean learning in order to facilitate swift and fluid perception of and interaction with the world. This learning is “slow” because is relies on meticulous accumulation of evidence for hypotheses at multiple time scales. It is “clean” because the learning slots into a hierarchy with clearly defined, general functional roles for time scales, for predictions of values, and for predictions of precisions.

The difference between swift and fluid processing and plodding and sluggish processing can easily be accommodated within a PEM system. Affordances are just causes of sensory input that, on the basis of prior learning, are strongly expected to give rise to high precision prediction error. To maintain Bayes optimality, the system gates sensory input accordingly, and strongly focuses both perceptual and active inference on these affordances. In this setting, PEM happens quickly, since highly precise distributions are easier to deal with computationally than imprecise ones. This means that the agent in question will obtain its expected states swiftly and fluidly.

Typically, the 4E preference for quick and dirty processing and affordances comes with a rejection of rich representational states (Clark 2008 , 2015 ). The point is that such representations cannot come about due to the bottleneck problem. Moreover, the appeal to affordance-based quick and dirty processing is thought to obviate the need for rich internal representations altogether as the world’s affordances in some sense are its own representation ( Brooks 1991 ).

On the PEM-based account of swift and fluid processing, internal representations are, however, necessary. Over time, multilayered representations are constructed and shaped, and Bayesian model selection picks the model with the best evidence as the representation of the world relative to which prediction error is minimized in active inference (this kind of approach is developed in more detail for PEM in Seth 2014 , 2015 ). Again, we get the result that PEM has the resources to speak to typical 4E discussions, but that it happens on the basis of representation and inference.

It could be that the brain builds rich representations as it learns about the world, and then gradually substitutes these much sparser and representation-poor, purpose-made representations that more directly tie in with and engage the environment. One argument here derives from Occam’s razor, in the sense that there are simplicity gains from opting for a simple over a complex, rich model ( Clark 2015 ). However, simplicity is not something additional to inference. Complex models are to be avoided because they are overfitted and thereby incur a prediction error cost in the longer run. How rich or simple a model should be is thus fully given by PEM in the first place.

In fact, there is reason to think the PEM account is preferable to the affordance-based account. It is true that swift and fluid processing is a salient and impressive aspect of human cognition. But so is the flexible way we shift between contexts, projects, beliefs, and actions. We might engage in attentive, fluid, and swift interaction for a period of time, but other beliefs and concerns always creep in and make it imperative to shift to another behavior. On the affordance-based account, it is not readily explained how the agent might disengage from a given set of affordances; the focus is at best on how representation-rich learning is needed before swift and fluid processing is possible, rather than the role of rich representation during swift and fluid processing. The agent seems tightly knitted to its environment, and it is not clear how the agent can step back and reconsider its current course of action.

In contrast, flexible cognition is a central motivation for adopting PEM’s hierarchical Bayesian inference in the first place. Active inference is driven by the most probable hypothesis at any given time. The system will have built up expectations not just for what the most likely causes of sensory input might be but also for the typical evolution of prediction error precision. In particular, there will be accumulated evidence that any given hypothesis under which prediction error is minimized at a certain time will have a limited life span—in essence, the system will know that it lives in a changing world where precise evidence for any given hypothesis will soon begin to be hard to find. For example, as the agent fluidly and swiftly catches baseballs, it will know that the sun will soon set and make the visual input imprecise. It will therefore begin accumulating evidence for the next hypothesis (e.g., “I am eating dinner”) under which evidence will soon begin to be accumulated and prediction error minimized.

This speaks to a crucial balance, which a PEM system must obtain. As prediction error is minimized in active inference, the hypothesis relative to which error is minimized is held stable. This means that, as prediction error is minimized, the world can in fact change “behind the scenes” to such an extent that it would eventually be better to abandon the current hypothesis and adopt a new one. Anticipating such change in the environment matters greatly to the agent because it should never engage in any behavior, no matter how swift and fluid, for so long that when it ceases the behavior, the world has changed in other respects and predictive error will be very large. A PEM agent therefore will be inclined to believe that the current state of affairs will change, and therefore the agent will intersperse active inference with perceptual inference, where the internal model is checked and the size of the overall prediction error is adjusted and tightened up before a new hypothesis is selected for active inference (see Hohwy 2013 ; Hohwy et al. 2015 ).

A hierarchical system operating with slow and clean processing can thus economically explain both swift and fluid, affordance-based cognition as well as flexible cognition. This is an important point to make in the context of PEM’s affinity to 4E cognition. The motivation for PEM is, in the end, the simple observation that we live in a changing world. Our world presents many different causes of our sensory input, and these causes interact with each other to create nonlinearities in the input; moreover, these interactions happen concurrently at many different time scales (e.g., “The setting sun makes the balls hard to see, but this time of the year the janitor often turns on the floodlights at the far pitch”). This complexity is what creates the need for hierarchical Bayesian inference in the first place: a rich internal model that keeps track of all these contingencies and can mix the various causes in the right way to anticipate the sensory input. This has a 4E-type ring to it: the cognitive system is the way it is because the agent’s world and body are the way they are. In particular, PEM is not the best solution for non-ecological, lab-style model environments where typically context and interactions between hidden causes are kept to a minimum. In other words, a machine learning researcher who never tests their system against the real world will have little impetus to build a PEM system. On 4E approaches, there is also a strong focus on real-world settings, but the response is typically to tie the agent very closely to its environment. This, however, makes it harder to see how the real world, and also that fact that the real world is a changing place, can be taken into consideration. PEM, in contrast, makes room for the changing world by retracting farther away from the world, into a vast internal model that seeks to represent the full richness of the world and the way it changes over many time scales. On the PEM conception of the agent’s place in the world, cognition is not a matter of being closely in tune with and driven by the sensory input. Rather, cognition is a matter of having richly represented expectations for the world and the body and seeking confirming feedback on those expectations through the senses.

The Mind and Things Without It

Both perception and action are inferential and representational. The PEM system’s process of minimizing prediction error implies that the sensory input is explained away on the basis of the evolving hypotheses of an internal model. The more the system can minimize its prediction error, the more it will accumulate evidence for its own model. This is a trivial observation: if I can minimize prediction error for my theory that my hamster has escaped, the more evidence I have for that theory. If we consider the PEM system an agent, then it acquires evidence for its own existence through its activities ( Friston 2010 ). Borrowing a term from philosophy of science, the PEM system can thus be said to be self-evidencing ( Hempel 1965 ; Hohwy 2016b ).

A self-evidencing system creates a sensory boundary between itself (i.e., the model) and the causes of its sensory input. This again is a trivial consequence of self-evidencing: there is something that garners evidence and then there is what the evidence is evidence of. Or again, in both perceptual and active inference there is something doing the inference and something being inferred. This boundary can also be described in terms of causal nets, where a set of inner states (i.e., brain states) can be said to have a “Markov blanket” ( Pearl 1988 ) consisting of the inner states’ parents (i.e., the sensory states) and their children and other parents of the children (i.e., the active states driving active inference) ( Friston 2013 ; Hohwy 2015 , 2017 ; causal Bayes nets must be acyclic, but brains have recurrent (cyclic) states; there are technical ways, such as dynamical Bayes nets, to deal with such problems). The activity of the states within a Markov blanket is wholly determined by the states of the blanket. In principle, nothing about the environmental states beyond the blanket need be known to know what the system is doing. By extension, in principle, only the states of the sensory organs need be known to know everything the mind does.

PEM then comes with a principled way of drawing a boundary between the mind and the outside world. If a particular state is part of what is doing the inference, then it must be within the sensory boundary, as a part of what approximates inference about outside causes of sensory input. This may relate to the vigorous debate about extended cognition ( Clark and Chalmers 1998 ; Clark 2008 ), which is the last member of 4E cognition to discuss.

Extended cognition is the idea that some objects, such as notebooks and smartphones, play such an integrated, memory-like function in the mental economy of some agents that, by parity of reasoning, they should be considered part of the agent’s mental states even though they reside outside the central nervous system. There is much discussion of this idea (see, e.g., Menary 2007 ; Adams and Aizawa 2008 ; Anderson et al. 2012 ; Spaulding 2012 ). PEM brings with it a new way of thinking about the role of such external objects. On the one hand, these objects are inferred (e.g., on the basis of the sensory input from the notebook) and as such they are outside the mental states of the system. On the other hand, if the extended cognition hypothesis is correct, they are within the sensory boundary, forming part of the inner states behind a Markov blanket inferring the hidden causes beyond it.

Interpreting purported cases of extended cognition according to PEM thus leaves two main options. There might be contradiction, since something cannot be both within and beyond the same boundary at the same time. Or, there might be multiple coexisting sensory boundaries. The second option is very interesting and very likely to be true, since Markov blankets occur easily. There is an associated cost, however: we have identified the inner states (or the model) with the agent, and if there are multiple Markov blankets then there are multiple agents coexisting at the same time. Though this may be true in a weak sense of agent, it is explanatorily messy. When asking which agent is acting, there would then be a multitude of correct answers, depending on how many nested Markov blankets are involved in the same action. This speaks in favor of using inference to the best explanation to identify the agent whose relatively invariant involvement accounts for most of observed behavior over time. It seems likely this more pragmatically identified agent would be the agent as specified by the model harbored just in the nervous system. This is the agent relative to which prediction error is minimized over the longer time scale, which as we saw is central to understanding predictive processing accounts in the first place (for discussion, see Hohwy 2016b ). Bringing this discussion back to extended cognition, the pragmatic method of identifying the agent suggests that there is no extended cognition, since the special objects in question are beyond the one Markov blanket. The more lax way of identifying agents suggests that extended cognition ambiguous, since the special objects are beyond some blankets and within others.

The existence of the sensory boundary or Markov blanket implies that perception and agency are confined to the inner states of the PEM system (wherever the boundary or boundaries of the system are located). Those inner states will mirror the states outside the boundary: the inner states will, through PEM, come to represent the worldly causes of the sensory input impinging at the system’s periphery. Conversely, through active inference, the outside states will come to conform to the expectations harbored in the internal states.

There is then an intriguing duality to this sensory boundary between mind and world. On the one hand, the boundary is epistemic (cf. self-evidencing): the worldly causes can only be known vicariously, through inference on sensory input. On the other hand, the boundary is characterized in causal terms (cf. Markov blanket): there is a dynamic coupling between mind and world, enabled through both perception and action.

This duality summarizes well why PEM is a good fit for many of the issues in 4E debates: PEM is able to throw light on embodied agents dynamically interacting with the environment in which they are embedded. This good fit with 4E cognition is, however, made possible precisely because PEM is inferential and representational.

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Meaning of hypothesize in English

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There is one orchid so strangely shaped that Darwin hypothesized a moth with a 12-inch proboscis that could dip down into its long , hollow tube .
Bartoshuk's latest study found some people experience greater " oral burn " from alcohol and she hypothesized that they were less likely to become alcoholics as a result .
It is hypothesized that, in this disease , a genetic defect forces calcium to remain outside of cells rather than entering them.
In the 18th century , natural scientists began to hypothesize about the Earth having a linear history rather than an eternally recurring pattern .
Children have a natural tendency to investigate , to hypothesize, and to experiment .
approximate
as much idiom
mark someone down as something
misjudgment
pluck something out of the air idiom
think of something
unguessable

hypothesize | American Dictionary

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Definition of hypothesize verb from the Oxford Advanced American Dictionary